Frailties are random effects in models for time-to-event data. They represent unobserved heterogeneity and can be used for two purposes: modelling dependence in clustered failures or recurrent events (shared frailties), and explaining lack of fit of univariate survival models, such as deviation from the proportional hazards assumption (individual frailties). Shared frailty models are increasingly used in applied research; a Google Scholar search of “shared frailty” yields 4490 results for 2016, compared to 3100 for 2011 and 1930 in 2006. Individual frailties on the other hand have recently been used to explain selection effects in epidemiological studies and to shed light on a number of paradoxes in epidemiology, like the obesity paradox.

The aim of this course is 1) to explain the selection effects of unobserved heterogeneity (individual frailty) in epidemiological studies and to illustrate how many strange artefacts can be explained from a frailty point of view, and 2) to show how to use shared frailty models in practice. With respect to the latter, we will review classical results and discuss practical aspects, such as software to fit frailty models, properties of different frailty distributions, and possible ways of extending such models.